import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import math
from functools import partial
# adv
from models.grad_reversal import ReverseLayerF
import pdb
__all__ = [
    'ResNet', 'resnet10', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
    'resnet152', 'resnet200'
]

def conv3x3x3(in_planes, out_planes, stride=1):
    # 3x3x3 convolution with padding
    return nn.Conv3d(
        in_planes,
        out_planes,
        kernel_size=3,
        stride=stride,
        padding=1,
        bias=False)


def downsample_basic_block(x, planes, stride):
    out = F.avg_pool3d(x, kernel_size=1, stride=stride)
    zero_pads = torch.Tensor(
        out.size(0), planes - out.size(1), out.size(2), out.size(3),
        out.size(4)).zero_()
    if isinstance(out.data, torch.cuda.FloatTensor):
        zero_pads = zero_pads.cuda()

    out = Variable(torch.cat([out.data, zero_pads], dim=1))

    return out


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm3d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3x3(planes, planes)
        self.bn2 = nn.BatchNorm3d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv3d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm3d(planes)
        self.conv2 = nn.Conv3d(
            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm3d(planes)
        self.conv3 = nn.Conv3d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm3d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class MLP_Block(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes):
        super(MLP_Block, self).__init__()
        self.fc = nn.Linear(inplanes, planes)
        self.bn = nn.BatchNorm1d(planes)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        out = self.fc(x)
        out = self.bn(out)
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self,
                 block,
                 layers,
                 sample_size,
                 sample_duration,
                 shortcut_type='B',
                 num_classes=400,
                 is_adv=False,   
                 is_human_mask_adv=False,              
                 alpha=0.0,
                 alpha_hm=0.0,
                 num_places_classes=365,                 
                 num_place_hidden_layers=1,
                 num_human_mask_adv_hidden_layers=1):
        self.inplanes = 64
        
        # adv
        self.is_adv = is_adv        
        self.is_human_mask_adv = is_human_mask_adv
        self.alpha = alpha
        self.alpha_hm = alpha_hm
        self.num_places_classes = num_places_classes
        
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv3d(
            3,
            64,
            kernel_size=7,
            stride=(1, 2, 2),
            padding=(3, 3, 3),
            bias=False)
        self.bn1 = nn.BatchNorm3d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0], shortcut_type)
        self.layer2 = self._make_layer(
            block, 128, layers[1], shortcut_type, stride=2)
        self.layer3 = self._make_layer(
            block, 256, layers[2], shortcut_type, stride=2)
        self.layer4 = self._make_layer(
            block, 512, layers[3], shortcut_type, stride=2)
        last_duration = int(math.ceil(sample_duration / 16))
        last_size = int(math.ceil(sample_size / 32))
        self.avgpool = nn.AvgPool3d(
            (last_duration, last_size, last_size), stride=1)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        # human mask adv
        if self.is_human_mask_adv:
            self.hm_mlp = nn.Sequential()
            self.hm_mlp = self._make_mlp_layer(MLP_Block, 512 * block.expansion, 512 * block.expansion, num_human_mask_adv_hidden_layers)
            self.hm_mlp.add_module('hm_last_fc', nn.Linear(512 * block.expansion, num_classes))
   
        # adv
        if self.is_adv:
            self.place_mlp = nn.Sequential()
            self.place_mlp = self._make_mlp_layer(MLP_Block, 512 * block.expansion, 512 * block.expansion, num_place_hidden_layers)
            self.place_mlp.add_module('p_last_fc', nn.Linear(512 * block.expansion, self.num_places_classes))
   
        for m in self.modules():
            if isinstance(m, nn.Conv3d):
                m.weight = nn.init.kaiming_normal(m.weight, mode='fan_out')
            elif isinstance(m, nn.BatchNorm3d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_mlp_layer(self, block, inplanes, planes, blocks):        
        layers = []
        layers.append(block(inplanes, planes))
        for i in range(1, blocks):
            layers.append(block(inplanes, planes))

        return nn.Sequential(*layers)

    def _make_layer(self, block, planes, blocks, shortcut_type, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            if shortcut_type == 'A':
                downsample = partial(
                    downsample_basic_block,
                    planes=planes * block.expansion,
                    stride=stride)
            else:
                downsample = nn.Sequential(
                    nn.Conv3d(
                        self.inplanes,
                        planes * block.expansion,
                        kernel_size=1,
                        stride=stride,
                        bias=False), nn.BatchNorm3d(planes * block.expansion))

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)

        x = x.view(x.size(0), -1)

        # adv
        if self.is_human_mask_adv:
            rev_x_hm = ReverseLayerF.apply(x, self.alpha_hm)
        if self.is_adv:            
            rev_x = ReverseLayerF.apply(x, self.alpha)                    
            dom_x = self.place_mlp(rev_x)
        
        x = self.fc(x)            

        if self.is_human_mask_adv and self.is_adv:
            hm_x = self.hm_mlp(rev_x_hm)
            return x, dom_x, hm_x
        elif self.is_adv:        
            return x, dom_x
        elif self.is_human_mask_adv:
            hm_x = self.hm_mlp(rev_x_hm)
            return x, hm_x
        else:            
            return x
        
def get_fine_tuning_parameters(model, ft_begin_index):
    if ft_begin_index == 0:
        return model.parameters()

    ft_module_names = []
    for i in range(ft_begin_index, 5):
        ft_module_names.append('layer{}'.format(i))
    ft_module_names.append('fc')

    parameters = []
    for k, v in model.named_parameters():
        for ft_module in ft_module_names:
            if ft_module in k:
                parameters.append({'params': v})
                break
        else:
            parameters.append({'params': v, 'lr': 0.0})
    
    return parameters

def get_adv_fine_tuning_parameters(model, ft_begin_index, new_layer_lr, not_replace_last_fc=False, is_human_mask_adv=False, slower_place_mlp=False, slower_hm_mlp=False):
    ft_module_names, frozen_module_names = [], []
    
    for i in range(0, ft_begin_index):
        frozen_module_names.append('layer{}'.format(i))
    for i in range(ft_begin_index, 5):
        ft_module_names.append('layer{}'.format(i))
    
    new_module_names = []

    if not slower_place_mlp:
        new_module_names.append('place_mlp')
    else:
        ft_module_names.append('place_mlp')
    if is_human_mask_adv:
        if not slower_hm_mlp:
            new_module_names.append('hm_mlp')
        else:
            ft_module_names.append('hm_mlp')
    if not not_replace_last_fc:
        new_module_names.append('fc')
    
    pretrained_parameters, new_parameters = [], []
    for k, v in model.named_parameters():
        for ft_module in ft_module_names:
            if ft_module in k:
                print('finetune params:{}'.format(k))
                pretrained_parameters.append(v)
                break
        else:                
            for new_module in new_module_names:
                if new_module in k:
                    print('new params:{}'.format(k))                    
                    new_parameters.append(v)
                    break
            else:
                for frozen_module in frozen_module_names:
                    if frozen_module in k:
                        print('frozen:{}'.format(k))                        
                        pretrained_parameters.append(v)
                        break
                else:
                    print('finetune params:{}'.format(k))
                    pretrained_parameters.append(v)
   
    return [pretrained_parameters, new_parameters]

def resnet10(**kwargs):
    """Constructs a ResNet-18 model.
    """
    model = ResNet(BasicBlock, [1, 1, 1, 1], **kwargs)
    return model


def resnet18(**kwargs):
    """Constructs a ResNet-18 model.
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    return model


def resnet34(**kwargs):
    """Constructs a ResNet-34 model.
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    return model


def resnet50(**kwargs):
    """Constructs a ResNet-50 model.
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    return model


def resnet101(**kwargs):
    """Constructs a ResNet-101 model.
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
    return model


def resnet152(**kwargs):
    """Constructs a ResNet-101 model.
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    return model


def resnet200(**kwargs):
    """Constructs a ResNet-101 model.
    """
    model = ResNet(Bottleneck, [3, 24, 36, 3], **kwargs)
    return model